Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds substantial behavioral context beyond that: it only uses the tool result content, returns verbatim evidence, provides explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), and discloses the extra LLM call cost. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient, with the core purpose front-loaded in the first clause. It then packs behavioral guarantees, return shapes, refusal reasons, usage guidance, and a cost comparison into a few sentences with no filler. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description's explicit return contract ({answer, evidence, confidence, source, fetched_at, refusal_reason}) and refusal enum fill that gap. Combined with complete parameter schema coverage and annotations covering read-only, idempotent, and open-world behavior, the agent has everything needed to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline of 3 applies. The description adds no parameter-specific meaning beyond the schema; it only refers to asking a natural language question and filling arguments. However, the schema fully documents the question parameter and its five aliases, so the description does not need to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly defines the mechanism (same routing as ask_pipeworx, then extracting the answer only from tool results) and distinguishes itself from the sibling ask_pipeworx with the key grounded-extraction behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage guidance is explicit and actionable: 'Use whenever an answer will be quoted, cited, or acted on' and lists concrete domains (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative ask_pipeworx and gives an explicit exclusion: 'prefer ask_pipeworx for casual lookups,' including the cost tradeoff of one extra LLM call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.